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Published April 2014 | metadata_only
Book Section - Chapter

Personalized Collaborative Clustering


We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.

Additional Information

Copyright is held by the International World Wide Web Conference Committee (IW3C2). This research was supported in part by ONR (PECASE) N000141010672 and ONR Young Investigator Program N00014-08-1-0752. The authors also thank Niki Kittur, Dafna Shahaf and Jing Xiang for valuable discussions and feedback.

Additional details

August 20, 2023
August 20, 2023